Object Detection Web App with TensorFlow, OpenCV and Flask

Object detection is one of the most powerful applications of computer vision, forming the foundation for technologies like self-driving cars, intelligent surveillance, image captioning, and robotics. If you’ve ever wondered how machines can identify and label objects in real time, this course will guide you through building your very own object detection web application from scratch.

You will begin by exploring the fundamentals of object detection and how it differs from traditional image classification. Using Python, TensorFlow, and OpenCV, you’ll work with the pre-trained COCO dataset to detect everyday objects efficiently. The course guides you step by step through the integration of pre-trained models with OpenCV to process images, highlight detected objects, and label them with confidence scores.

Once the core detection system is ready, you’ll take it to the next level by deploying it as a Flask-based web application. This will allow users to upload images via a web interface and instantly see detected objects marked on their pictures. Through this process, you’ll not only strengthen your machine learning and deep learning knowledge but also gain valuable experience in web deployment — a skill in high demand today.

By the end of the course, you will:

• Understand object detection concepts and real-world use cases

• Work with TensorFlow and OpenCV for computer vision tasks

• Use transfer learning and pre-trained models effectively

• Deploy your model as a Flask web app for practical use

No prior deep learning expertise is required — just Python basics and curiosity. Start your journey into computer vision today!